EZ-QC: A User-Friendly Tool for Multi-site, Multimodal Neuroimaging Data Triage and Visual Quality Control

Motivation: Multisite MRI studies face QC delays and inconsistencies due to data volume and variations in rater expertise. Prioritizing scans for review and supporting consistent QC could enhance data quality. Goal(s): Develop EZ-QC, a tool that triages data for efficient QC review while promoting consistency across raters. Approach: EZ-QC integrates with MRIQC to prioritize presenting data that could indicate systematic data-collection issues. It also includes tools to ensure consistent QC across raters, supporting reliable assessments. Results: EZ-QC improves QC efficiency by prioritizing the most informative data, providing structured training, and tracking interrater reliability, ensuring consistent QC for large, multisite datasets. Impact: EZ-QC improves QC efficiency by prioritizing data that are most likely to indicate systemic errors and standardizing rater training. This approach supports consistent and efficient QC, helping to maximize usable data and ensure reliable data quality in large, multisite studies.